Selecting the optimal reference satellite is an important component of high-precision relat/ve positioning because the reference satellite directly influences the strength of the normal equation. The reference satelli...Selecting the optimal reference satellite is an important component of high-precision relat/ve positioning because the reference satellite directly influences the strength of the normal equation. The reference satellite selection methods based on elevation and positional dilution of precision (PDOP) value were compared. Results show that all the above methods cannot select the optimal reference satellite. We introduce condition number of the design matrix in the reference satellite selection method to improve structure of the normal equation, because condition number can indicate the ill condition of the normal equation. The experimental results show that the new method can improve positioning accuracy and reliability in precise relative positioning.展开更多
Designing the optimal distribution of Global Navigation Satellite System(GNSS)ground stations is crucial for determining the satellite orbit,satellite clock and Earth Rotation Parameters(ERP)at a desired precision usi...Designing the optimal distribution of Global Navigation Satellite System(GNSS)ground stations is crucial for determining the satellite orbit,satellite clock and Earth Rotation Parameters(ERP)at a desired precision using a limited number of stations.In this work,a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed,named the minimum Orbit and ERP Dilution of Precision Factor(OEDOP)criterion.To quickly identify the specific station locations for the optimal station distribution on a map,a method for the rapid determination of the selected station locations is developed,which is based on the map grid zooming and heuristic technique.Using the minimum OEDOP criterion and the proposed method for the rapid determination of optimal station locations,an optimal or near-optimal station distribution scheme for 17 newly built BeiDou Navigation Satellite System(BDS)global tracking stations is suggested.To verify the proposed criterion and method,real GNSS data are processed.The results show that the minimum OEDOP criterion is valid,as the smaller the value of OEDOP,the better the precision of the satellite orbit and ERP determination.Relative to the exhaustive method,the proposed method significantly improves the computational efficiency of the optimal station location determination.In the case of 3 newly built stations,the computational efficiency of the proposed method is 35 times greater than that of the exhaustive method.As the number of stations increases,the improvement in the computational efficiency becomes increasingly obvious.展开更多
The seven co-located sites of the Crustal Movement Observation Network of China(CMONOC) in Shanghai, Wuhan, Kunming, Beijing, Xi'an, Changchun, and Urumqi are equipped with Global Navigation Satellite System(GNSS...The seven co-located sites of the Crustal Movement Observation Network of China(CMONOC) in Shanghai, Wuhan, Kunming, Beijing, Xi'an, Changchun, and Urumqi are equipped with Global Navigation Satellite System(GNSS), very long baseline interferometry(VLBI), and satellite laser ranging(SLR) equipment. Co-location surveying of these sites was performed in 2012 and the accuracies of the solved tie vectors are approximately 5 mm.This paper proposes a mathematical model that handles the least squares adjustment of the 3D control network and calculates the tie vectors in one step, using all the available constraints in the adjustment. Using the new mathematical model, local tie vectors can be more precisely determined and their covariance more reasonably estimated.展开更多
A neural network model of the Global Navigation Satellite System - vertical total electron content (GNSS-VTEC) over Nigeria is developed. A new approach that has been utilized in this work is the consideration of th...A neural network model of the Global Navigation Satellite System - vertical total electron content (GNSS-VTEC) over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere's (IRI's) critical plasma frequency (foF2) parameter as an additional neuron for the network's input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time (DST) and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network (NIGNET) were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI's foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.展开更多
基金partially sponsored by the National 973 Project of China(2013CB733303)partially supported by the postgraduate independent exploration project of Central South University(2014zzts249)
文摘Selecting the optimal reference satellite is an important component of high-precision relat/ve positioning because the reference satellite directly influences the strength of the normal equation. The reference satellite selection methods based on elevation and positional dilution of precision (PDOP) value were compared. Results show that all the above methods cannot select the optimal reference satellite. We introduce condition number of the design matrix in the reference satellite selection method to improve structure of the normal equation, because condition number can indicate the ill condition of the normal equation. The experimental results show that the new method can improve positioning accuracy and reliability in precise relative positioning.
基金This work was supported by“The National Natural Science Foundation of China(No.41404033)”“The National Science and Technology Basic Work of China(No.2015FY310200)”+1 种基金“The State Key Program of National Natural Science Foundation of China(No.41730109)”“The Jiangsu Dual Creative Teams Program Project Awarded in 2017”and thanks for the data from IGS and iGMAS。
文摘Designing the optimal distribution of Global Navigation Satellite System(GNSS)ground stations is crucial for determining the satellite orbit,satellite clock and Earth Rotation Parameters(ERP)at a desired precision using a limited number of stations.In this work,a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed,named the minimum Orbit and ERP Dilution of Precision Factor(OEDOP)criterion.To quickly identify the specific station locations for the optimal station distribution on a map,a method for the rapid determination of the selected station locations is developed,which is based on the map grid zooming and heuristic technique.Using the minimum OEDOP criterion and the proposed method for the rapid determination of optimal station locations,an optimal or near-optimal station distribution scheme for 17 newly built BeiDou Navigation Satellite System(BDS)global tracking stations is suggested.To verify the proposed criterion and method,real GNSS data are processed.The results show that the minimum OEDOP criterion is valid,as the smaller the value of OEDOP,the better the precision of the satellite orbit and ERP determination.Relative to the exhaustive method,the proposed method significantly improves the computational efficiency of the optimal station location determination.In the case of 3 newly built stations,the computational efficiency of the proposed method is 35 times greater than that of the exhaustive method.As the number of stations increases,the improvement in the computational efficiency becomes increasingly obvious.
基金sponsored by the Crustal Movement Observation Network of China(CMONOC)partially supported by the Natural Science Foundation of China(41274035,41174023)
文摘The seven co-located sites of the Crustal Movement Observation Network of China(CMONOC) in Shanghai, Wuhan, Kunming, Beijing, Xi'an, Changchun, and Urumqi are equipped with Global Navigation Satellite System(GNSS), very long baseline interferometry(VLBI), and satellite laser ranging(SLR) equipment. Co-location surveying of these sites was performed in 2012 and the accuracies of the solved tie vectors are approximately 5 mm.This paper proposes a mathematical model that handles the least squares adjustment of the 3D control network and calculates the tie vectors in one step, using all the available constraints in the adjustment. Using the new mathematical model, local tie vectors can be more precisely determined and their covariance more reasonably estimated.
文摘A neural network model of the Global Navigation Satellite System - vertical total electron content (GNSS-VTEC) over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere's (IRI's) critical plasma frequency (foF2) parameter as an additional neuron for the network's input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time (DST) and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network (NIGNET) were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI's foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.